Tensorflow 2.3.0无法检测到GPU [英] Tensorflow 2.3.0 does not detect GPU

问题描述

张量流未检测到GPU卡.我已按照Nvidia网站和tensorflow/install/gpu上建议的步骤进行操作.

The tensorflow does not detect the GPU card. I have following the procedures suggest at Nvidia website and tensorflow/install/gpu.

我该如何解决?

我正在使用以下软件包和驱动器:

I am using the following packages and drives:

NVIDIA

[nvcc: NVIDIA (R) Cuda compiler driver

Copyright (c) 2005-2019 NVIDIA Corporation

Built on Sun_Jul_28_19:12:52_Pacific_Daylight_Time_2019

Cuda compilation tools, release 10.1, V10.1.243][1]

Cudnn 版本8.0.2

张量流

Name                      Version                   Build  Channel
tensorflow                2.3.0                    pypi_0    pypi
tensorflow-addons         0.11.1                   pypi_0    pypi
tensorflow-estimator      2.3.0                    pypi_0    pypi

我使用以下代码进行检查；

I use the following code to check it;

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.

IPython 7.17.0 -- An enhanced Interactive Python.

from tensorflow.python.client import device_lib
device_lib.list_local_devices()

结果

2020-08-20 22:58:38.419555: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 
Out[1]:  [name: "/device:CPU:0"  device_type: "CPU"  memory_limit: 268435456  locality {  }  incarnation: 12639439165040732604,  name: "/device:XLA_CPU:0"  device_type: "XLA_CPU"  memory_limit: 17179869184  locality {  }  incarnation: 2249215130251849864  physical_device_desc: "device: XLA_CPU device",  name: "/device:XLA_GPU:0"  device_type: "XLA_GPU"  memory_limit: 17179869184  locality {  }  incarnation: 7640064762024919839  physical_device_desc: "device: XLA_GPU device"]
2020-08-20 22:58:38.419555: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-08-20 22:58:40.332579: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-08-20 22:58:40.340307: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22481a47710 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-08-20 22:58:40.341741: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-08-20 22:58:40.342711: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2020-08-20 22:58:40.362324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:  pciBusID: 0000:01:00.0 name: GeForce GTX 1050 computeCapability: 6.1 coreClock: 1.493GHz coreCount: 5 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s 
2020-08-20 22:58:40.362354: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 
2020-08-20 22:58:40.366447: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll 
2020-08-20 22:58:40.369790: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll 
2020-08-20 22:58:40.370968: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll 
2020-08-20 22:58:40.374957: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll 
2020-08-20 22:58:40.377382: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll 
2020-08-20 22:58:40.378955: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudnn64_7.dll'; dlerror: cudnn64_7.dll not found
2020-08-20 22:58:40.378977: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1753] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices...
2020-08-20 22:58:40.455688: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-08-20 22:58:40.455717: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2020-08-20 22:58:40.455728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2020-08-20 22:58:40.458391: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22490b5c830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-08-20 22:58:40.458412: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050, Compute Capability 6.1

推荐答案

检查软件要求:此处

它说cudnn版本= 7.6

It says cudnn version = 7.6

确保已安装所有c ++可再发行文件-这里

Make sure you have installed all the c++ redistributables - Here

确保您具有适当的python版本.-此处

Make sure you have the appropriate python version. - Here

最后，请确保已在系统中将路径设置为Cuda和cudnn.

Finally, make sure you have set the path to Cuda and cudnn in your system.

确保已安装的NVIDIA软件包与上面列出的版本匹配.特别是，如果没有cuDNN64_7.dll文件.要使用其他版本，请参阅Windows版本来自源代码指南.

这在TensorFlow文档中有所说明，这似乎是您的问题

This is stated in TensorFlow documentation which seems to be your issue

这篇关于Tensorflow 2.3.0无法检测到GPU的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！